Machine Learning Applications in Finance

The financial market was one of the first fields to adopt machine learning algorithms, all the way back in the 1970s. Ever since, people have been using machines to spot patterns and predict market outcomes. Nowadays, with deep machine learning technologies on the rise, more and more applications are becoming available to the finance sphere. Let’s take a look at some of them and identify the most promising trends.

Uses of Machine Learning in FinanceImage source: Pixabay

Uses of Machine Learning in Finance

Machine learning is a subset of data science that makes predictions based on statistical models and insights gained from them. No explicit programming is needed: modern machines have the capability to learn from experience.

The technology was sure to find its application in various spheres of business and industry, and finance might be one of the best fits for it just because of how machine learning works. The accuracy of results fully depends on the amount of data available for analysis, and financial databases tend to be enormous and highly detailed. This is what makes the financial applications of machine learning so successful in prospect.

The main financial applications of machine learning are as follows:

Algorithmic Trading

This technology originated back in the ’70s. Also known as automated trading systems, algorithmic trading uses artificial intelligence systems to make trading decisions. The approach is characterized by high speed, often reaching billions of daily trades. Machine learning is believed to be used by many financial institutions, although it is not highly publicized.

While being one of the most common uses of machine learning in fintech, algorithmic trading is, however, not a magic wand for accurate predictions. At a time when PR accidents involving a company, or even a single tweet from a public person, have been known to wreck the stock markets overnight, there is only so much that machines can predict from pure statistical data.

Security and Fraud Protocols

Now that the internet is almost universally available, banks and companies have started storing more and more valuable data online, and data security risks are becoming even more of a constant threat. Fraud detection systems need to evolve, and the ability to learn and calibrate to new security threats is a very important factor in this.

As for use cases, a good example of a company that employs machine learning for fraud detection is the financial giant PayPal. Their deep machine learning systems keep their fraud rate notably low. According to a 2015 study, the PayPal fraud rate was at 0.32% of revenue, compared to the average 1.32% seen by merchants.

financial applications of machine learning

Machine learning can also be used to detect abnormal activities and send out detection signals. With the increasing number of inventive ways security can be breached, the use of machine learning in finance security systems promises to become a regular thing within the next few years.


Not having anything to do with robots as we know them, robo-advisors are algorithms that are built to adjust a financial portfolio to a user’s goals.

This is how it works: a user enters their goals into the program (i.e. collecting $300,000 by the age of 50), along with some personal data such as current income, age, and funds at their disposal; the robo-advisor then automatically selects the best financial instruments and assets to invest in for the purpose of achieving that particular goal. Such a system is able to adapt to changes on the market in real time, constantly making corrections to find the best fit for the user.

Since their use is more affordable than human advisors’ services, robo-advisors are becoming popular with the young generation, who might be more comfortable investing with the help of AI than another human. It is estimated that 10% of all managed account assets will be managed by robo-advisers by 2020.

uses of machine learning in fintech

Loans and Insurance

Loans are always a high-risk operation, and the need to correctly predict the potential result of such a deal is high. Machine learning algorithms can analyze a huge set of data concerning potential debtor’s background, both general (their occupation, age, marital status, etc.) and financial (their previous loan history, recent accidents, etc.) to produce an assessment of their solvency.

Besides assessing the risks for each particular case, machine learning also has a more global application here: detecting trends that could influence the entire sphere. For example, a machine analysis could show whether a specific demographic is becoming increasingly more likely to default.

This application of machine learning in banking is mostly used by large companies at the moment, but promises to become very popular in the future, possibly providing a replacement for a significant number of human jobs.

Process Automation

This is the most common use of machine learning in fintech. It allows increasing efficiency by replacing manual work and automating repetitive tasks. This is effective when it comes to optimizing costs, improving client experience and the level of service provided in general.

Some of the applications of machine learning in finance when it comes to process automation are:

  • chatbots;
  • automation of paperwork;
  • automation of call centers;
  • employee training.

Chatbots in particular are a very promising technology when it comes to the benefits of machine learning in banking. Companies are already working on finance-specific bots that could provide customers with detailed information on their finances, answering queries such as “How much has our family spent on groceries this year?” A notable example is Cleo, an AI-powered chatbot that works as a replacement for your banking app.

machine learning in finance

Machine Learning in Finance: Advantages and Disadvantages

The reasons to implement machine learning in your business seem obvious. Nothing but machines can provide the following advantages:

  • the automation of the processes leading to significantly reduced operational costs;
  • better productivity leading to increased revenues and a better user experience;
  • stronger security creating more favorable conditions for both business and clients.

There are, however, some downsides you need to consider as well. First of all, there is the cost of implementing high-quality deep machine learning in fintech. Whereas a giant like PayPal can afford the expense, smaller companies may not be able to.

The human factor is another thing to consider. Getting rid of it is a good thing when it comes to automating the processes, but less good when it comes to stock trading, which can be influenced by outside human factors that machines are unable to account for.

How to Get the Best out of Using Machine Learning in Finance

Using machine learning systems to their full extent is a complicated task that even big companies may have trouble with.

In order to be able to make the machine learning technology work efficiently, a company needs to have clearly defined business KPIs, a clear idea of how data science functions, and a vision of the end product; otherwise it is quite possible to drain your budget without achieving significant results.

Hiring good data engineers is the first step towards the realization of your idea. You need to collect as much data as you can in order for your machine learning project to be successful. Data scientists will help you come up with realistic KPIs and make estimates, bringing you a step closer to your goal. Then you will need to involve other professionals, such as machine learning engineers and, of course, developers.

Alternatively, you can use a third-party machine learning solution. Google, Amazon, Microsoft and IBM have all come up with machine learning software that they offer as a product. For less ambitious projects, these solutions can be enough.

Final Thoughts

Machine learning is a promising subset of data science, which is rapidly undergoing development. The financial market is especially well suited for it, and the sphere of its potential applications in finance is constantly growing. Implementing a machine learning solution into your project might be an intimidating task, but it is well worth it. The key is choosing a professional team to help you avoid the pitfalls.

Contact us now to discuss your project, and find out how machine learning can help your particular service stay on top of the market.